{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/analysis-of-clinical-dosimetric-and-radiomic","title":"Analysis of clinical, dosimetric and radiomic features for predicting local failure after stereotactic radiotherapy of brain metastases in malignant melanoma","arxiv_id":"2405.20825","date":"2024-05-31","proceeding":null,"authors":["Nanna E. Hartong","Ilias Sachpazidis","Oliver Blanck","Lucas Etzel","Jan C. Peeken","Stephanie E. Combs","Horst Urbach","Maxim Zaitsev","Dimos Baltas","Ilinca Popp","Anca-Ligia Grosu","Tobias Fechter"],"abstract":"Background: This study aimed to predict lesion-specific outcomes after stereotactic radiotherapy (SRT) in patients with brain metastases from malignant melanoma (MBM), using clinical, dosimetric pretherapeutic MRI data. Methods: In this multicenter retrospective study, 517 MBM from 130 patients treated with single-fraction or hypofractionated SRT across three centers were analyzed. From contrast-enhanced T1-weighted MRI, 1576 radiomic features (RF) were extracted per lesion - 788 from the gross tumor volume (GTV), 788 from a 3 mm peritumoral margin. Clinical data, radiation dose and RF from one center were used for feature selection and model development via nested cross-validation; external validation was performed using the other two centers. Results: Local failure occurred in 72 of 517 lesions (13.9%). Predictive models based on clinical data (model 1), RF (model 2), or both (model 3) achieved c-indices of 0.60 +/- 0.15, 0.65 +/- 0.11, and 0.65 +/- 0.12. RF-based models outperformed the clinical model, while dosimetric data alone were not predictive. Most predictive RF came from the peritumoral margin (92%) vs. GTV (76%). On the first external dataset, all models performed similarly (c-index: 0.60-0.63), but showed poor generalization on the second (c-index < 0.50), likely due to differences in patient characteristics and imaging protocols. Conclusions: Information extracted from pretherapeutic MRI, particularly from the peritumoral area, can support accurate prediction of lesion-specific outcomes after SRT in MBM. When combined with clinical data, these imaging-derived markers offer valuable prognostic insights. However, generalizability remains challenging by heterogeneity in patient populations and MRI protocols.","url_abs":"https://arxiv.org/abs/2405.20825v2","url_pdf":"https://arxiv.org/pdf/2405.20825v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"analysis-of-clinical-dosimetric-and-radiomic","repo_url":"https://github.com/tofec/radiomicsmm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[{"method_slug":"feature-selection","method_name":"Feature Selection"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}